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In this paper the problem of service center servers high energy consumption is tackled by proposing a time series based CPU dynamic frequency scaling algorithm. The algorithm senses the workload changes and adapts the CPU power states thus minimizing the CPU energy consumption. Our solution analyzes the CPU workload time series for identifying the frequent workload patterns. For each frequent pattern, the corresponding dynamic frequency scaling actions are determined and associated using information about the pattern's sub-sequences trends. A workload characterization function is defined and used to identify the pattern trends. To identify the membership of the new CPU workload observations to a frequent CPU workload pattern, a sliding window based method is used. If such a match is found, the dynamic frequency scaling actions associated to the frequent pattern are executed and the pattern occurrence probability is increased.
Cioara et al. (Mon,) studied this question.